docstr: Clip gradient to a maximum L2-Norm during the training process.
  In order to take effect, it needs to be called before fit.
  
  :param clip_norm: Gradient L2-Norm threshold.
  :return:
docstr_tok: ["Clip","gradient","to","a","maximum","L2-Norm","during","the","training","process",".","In","order","to","take","effect",",","it","needs","to","be","called","before","fit",".",":","param","clip_norm",":","Gradient","L2-Norm","threshold",".",":","return",":"]
code: def set_l2_norm_gradient_clipping(self, clip_norm):
      """
      Clip gradient to a maximum L2-Norm during the training process.
      In order to take effect, it needs to be called before fit.
  
      :param clip_norm: Gradient L2-Norm threshold.
      :return:
      """
      self.nn_estimator.setGradientClippingByL2Norm(clip_norm)
      self.estimator.set_l2_norm_gradient_clipping(clip_norm)
  
imports: from zoo.orca.learn.metrics import Metric
  from zoo.orca.learn.utils import bigdl_metric_results_to_dict
  from zoo.pipeline.nnframes import NNEstimator, NNModel
  from zoo.pipeline.estimator import Estimator
  from zoo.orca.learn.spark_estimator import Estimator
  from zoo.orca.data import SparkXShards
  from bigdl.optim.optimizer import MaxEpoch
  from zoo.feature.common import FeatureSet
  from zoo.orca.learn.metrics import Accuracy
  from pyspark.sql.dataframe import DataFrame
label_desc: Use image node_id 11 for calling the BigDLEstimator obj's underlying member method code with example usage: obj.set_l2_norm_gradient_clipping(clip_norm) without return types